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Early fire detection using convolutional neural networks during surveillance for effective disaster management
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“For instance, the outcomes of the Mivia dataset experiment achieved an accuracy of 95.32%, outperforming another CNN-based system [132] that achieved 94.39% accuracy.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [132]
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Region-based convolutional networks for accurate object detection and segmentation
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- D. Guha-Sapir, F. Vos, R. Below, S. Penserre, Annual disaster statistical review 2015: the numbers and trends, 2015. http://www.cred.be/sites/default/files/ADSR_2015.pdf.
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Items 301–400 of 558
Adão, T. O., Pinho, T. M., Pádua, L., Santos, N., Sousa, A., Sousa, J. J., & Peres, E. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-3-w8-9-2019 · Cited by 5
“In the work of Muhammad et al. (2018), convolutional neural networks (CNN) fine-tuned for closed-circuit television cameras were applied towards fire detection.”
BackgroundMethodA Comparative Study on Disaster Detection from Social Media Images Using Deep Learning
Arif, A., Omar, A., Ashraf, S., Rahman, A. K. M. M., Amin, M. A., & Ali, A. A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-2188-1_38 · Cited by 5
Intelligent IoT Monitoring System Using Rule-Based for Decision Supports in Fired Forest Images
Pham, H. V., & Nguyen, Q. H. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-77424-0_30 · Cited by 5
Application Research of Image Feature Recognition Algorithm in Visual Image Recognition
Yu, W., Peng, J., Li, J., Deng, Y., & Li, H. (2022) · IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) · doi:10.1109/tocs56154.2022.10016132 · Cited by 5
“It is usually defined as a feature of an image region or a measure of the relationship between pixels in a region [12-13].”
BackgroundA Novel Transfer Learning based CNN Model for Wildfire Susceptibility Prediction
Oak, O., Nazre, R., Naigaonkar, S., Sawant, S. T., & Joshi, A. D. (2024) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet61516.2024.10593496 · Cited by 5
Khosravi, H., Shafie, M. R., Raihan, A. S., Syed, M. A. B., & Ahmed, I. (2023) · Preprints.org · doi:10.20944/preprints202312.0577.v1 · Cited by 5
Real-Time Active Fire Detection in the Pantanal Biome, Brazil, Using Convolutional Neural Networks
da Costa, D. C., Batista, L. V., da Silva, R. M., & Santos, C. A. G. (2025) · Fire Technology · doi:10.1007/s10694-025-01722-0 · Cited by 4
YCFA-Net: A unified framework for vehicle detection and fire anomaly recognition in tunnel scenarios
Liu, L., Song, X., Song, H., Sun, S., Zhang, Z., Gu, Z., Wei, B., Lei, Q., & Luo, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127443 · Cited by 5
Zhang, L., Zhu, Y., Yan, X., Wu, H., & Li, K. (2022) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-022-01123-1 · Cited by 4
“Muhammad et al. [14] proposed an early fire detection framework, which improved the applicability to different types of the fire detection by adding depth features of CNN. Nemalidinne and Gupta [15] used spatial fuzzy C-means clustering to extract fire area of the fused visible light and infrared…”
MethodPahuja, N. K., & Rivero, M. H. (2022) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci58124.2022.00039 · Cited by 4
A novel deep convolutional encoder–decoder network: application to moving object detection in videos
Ganivada, A., & Yara, S. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08956-5 · Cited by 4
“consists of five categories namely, object acquisition [4], target detection [22], detection of abnormal activities [34],”
BackgroundFireSonic: Design and Implementation of an Ultrasound Sensing-Based Fire Type Identification System
Wang, Z., Wang, Y., Liao, M., Sun, Y. M., Wang, S., Sun, X., Shi, X., Kang, Y., Tian, M., Bao, T., et al. (2024) · Sensors · doi:10.3390/s24134360 · Cited by 4
“Existing fire detection methods primarily rely on smoke [6–9], vision [10–14], radio frequency [15–17], and acoustics [18–22].”
BackgroundYu, J., Huang, H., Ma, Y., Wu, Y., Chen, J., Zhang, R., Xu, X., Lv, Z., & Yin, G. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3492347 · Cited by 4
“Since many pretrained CNN backbones can be used as classification networks, some researchers choose to fine-tune the pretrained network by modifying the final classifier [7], [19], [20], while others redesign the structure of neural network [43], [44].”
Singh, K., Sharma, E., Baath, P. K., Kaur, K., & Singh, H. (2024) · International Journal of Sensors Wireless Communications and Control · doi:10.2174/0122103279339983241028073645 · Cited by 4
A Blurry Image Recognition Method for Straw Burning Detection
Ye, X., Yu, F. Q., Zhou, C., & Jiang, M. (2020) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/icsip49896.2020.9339292 · Cited by 4
“[4] and [5] proposed their methods based on deep convolutional neural networks.”
MethodDeep Convolutional Neural Networks based on Manifold for Smoke Recognition
Cheng, M., Ma, P., He, R., Chen, J., Zhang, Z., & Huang, J. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534297 · Cited by 4
“Among such disasters, fire is one of the commonly happening events, whose detection at early stages during surveillance can avoid home fires and fire disasters [1].”
BackgroundAn Improved Deep Learning Model for Early Fire and Smoke Detection on Edge Vision Unit
Shoukry, N., Ehab, F., & Salem, M. A. ‐. (2021) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/icicis52592.2021.9694193 · Cited by 4
A Comparison of Deep Learning Methods for Vision-based Fire Detection in Surveillance System
Wahyono, W., Harjoko, A., Dharmawan, A., Kosala, G., & Pranata, P. Y. (2021) · International Conference on Future Networks and Distributed Systems (ICFNDS) · doi:10.1145/3508072.3508074 · Cited by 4
Fire Detection by using DenseNet 201 algorithm and Surveillance Cameras images
Muhammad, S. S., & Alrikabi, J. M. (2024) · Journal of Al-Qadisiyah for Computer Science and Mathematics · doi:10.29304/jqcsm.2024.16.11437 · Cited by 4
“Khan Muhammad et al. (2018) In [7] The article propose a fire-detection framework using a convolutional neural network architecture with surveillance videos.”
MethodIntelligent Video Analytic Based Framework for Multi-view Video Summarization
Parikh, V., & Sharma, P. (2022) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/120150 · Cited by 4
“Applications of video summarization are in various disciplines like generating soccer match highlights [16], cattle behavior analysis from video [17], information retrieval and surveillance systems [18], multi-person tracking using representation learning [19], disaster management [20], and abnormal event detection [2…”
BackgroundThe Wetland Map Validation for Ukraine
Шелестов, А. Ю., Яйлимова, Г. О., Яйлимов, Б. Я., & Чирков, А. В. (2021) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs53288.2021.9661037 · Cited by 4
“These are annual land cover and crop types maps [1], maps of deforestation [2], [3], maps of fires and floods [4], [5], the crop state maps [6] and land degradation maps [7].”
BackgroundFire Object Detection Algorithm Based on Improved YOLOv3-tiny
Li, Y., Rong, L., Li, R., & Xu, Y. (2022) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda55098.2022.9778892 · Cited by 4
OpenSceneVLAD: Appearance Invariant, Open Set Scene Classification
Smith, W. H., Milford, M. J., McDonald-Maier, K. D., Ehsan, S., & Fisher, R. B. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812248 · Cited by 4
“It has applications for content based image retrieval [1], robot navigation [2] and disaster detection [3].”
MethodA Disaster Prediction Ensemble Classifier Using Artificial Intelligence
Bharathi, V., Saranya, A., Shukla, G., Shekhawat, D., Awasthi, V., & Kumar, C. N. S. V. (2024) · International Conference on Modeling, Simulation & Intelligent Computing (MoSICom) · doi:10.1109/mosicom63082.2024.10880975 · Cited by 3
Intelligent decision support system for automated fire monitoring in technical systems
Skvorcov, A., ANUREVA, M. S., & Solodovnikov, A. S. (2025) · Modeling of systems and processes · doi:10.12737/2219-0767-2025-85-96 · Cited by 3
Real-time CCTV-based deep learning for early detection of lithium-ion battery fires
Ali, M. M. E. H., Tahtali, M., & Ghodrat, M. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.238452 · Cited by 3
Masked autoencoder-based vision framework for robust fire detection in complex environments
Wang, H., Fayaz, M., Ahmad, M. A., Li, Y., Nguyen, T. N., & Dang, L. M. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.108019 · Cited by 3
Kruger, S., & Schutte, C. F. (2026) · Journal of Management & Organization · doi:10.1017/jmo.2025.10079 · Cited by 4
“The (iv) digital humanitarian innovation and sustainability (yellow) cluster is underpinned by Muhammad, Ahmad and Baik (2018), who explored how digital innovation intersects with sustainability-focused humanitarian approaches.”
LiteUAV-Det: A lightweight network for robust small-object detection in complex aerial scenes
Jobaer, S., Muzahid, A. A. M., Hussain, M. A. I., Ahmed, F., Bai, X., Han, H., & Sohel, F. A. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133782 · Cited by 3
Optimized Deep Learning Model for Fire Semantic Segmentation
Li, S., Liu, P., Yan, Q., & Qian, R. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.026498 · Cited by 3
FireNet-Micro: Compact Fire Detection Model with High Recall
Marakkaparambil, S. I., Rameshkumar, R., Dinesh, M. P., Aslam, A., & Ansari, M. S. (2024) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-47508-5_6 · Cited by 3
Time Series Forecasting Techniques for Climate Trend Prediction
Zakari, R. Y., Lawal, Z. K., Kalinaki, K., & Maiwada, U. D. (2024) · Advances in environmental engineering and green technologies book series · doi:10.4018/979-8-3693-2351-9.ch014 · Cited by 4
A novel approach based on convolutional neural networks ensemble for fire detection
Belarbi, F., Hassini, A., & Benamara, N. K. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03508-3 · Cited by 3
“For instance, Khan.et al [20 ] suggested fire detection study in surveillance cameras, using an architecture similar to AlexNet.”
FireDA: A Domain Adaptation-Based Method for Forest Fire Recognition with Limited Labeled Scenarios
Yan, Z., Zheng, X., Li, W., Wang, L., Ding, P., Zhang, L., Yin, M., & Xiao-wei, W. (2024) · Forests · doi:10.3390/f15101684 · Cited by 3
“K. Muham-mad et al. [40] proposed an early video detection algorithm based on AlexNet, pre-trained on ImageNet, and reliant on surveillance cameras to establish a disaster management system.”
Deep feature response discriminative calibration
Xu, W., Qiu, T., Zhou, L., Feng, Z., Song, M., & Wang, H. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128848 · Cited by 3
Medewar, A. G., Sawarkar, A. D., & Kshirsagar, U. V. (2024) · Cureus Journal of Computer Science. · doi:10.7759/s44389-024-00359-4 · Cited by 3
“Predictive Analytics for Fire Behavior - AI models predict fire behavior using historical and real-time IoT data [24].”
Li, K., Yuan, F., & Wang, C. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3572007 · Cited by 3
“In recent years, researchers have developed various fire detection systems [1] and have devoted substantial efforts to continuously optimize their performance [2] for preventing fires and minimizing losses.”
FireMamba: An efficient visual representation learning framework for fire detection
Khan, S. U., Danish, S., Iqbal, E., Gupta, D., Zweiri, Y., & Abdulrahman, Y. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.10.001 · Cited by 3
Kim, J., Lee, C., Park, S.-H., Lee, J.-H., & Hong, C.-H. (2020) · Journal of the Korea Academia-Industrial cooperation Society · doi:10.5762/kais.2020.21.12.320 · Cited by 3
An Activity Recognition Framework for Overlapping Activities using Transfer Learning
Bilal, M., Maqsood, M., Mehmood, I., Javaid, M., & Rho, S. (2020) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci51800.2020.00129 · Cited by 3
“The resulting features-vector will be high-dimensional that represent actions from a video sequence in the raw form [9, 25].”
BackgroundMethodQ-rough sets, flicker modeling and unsupervised fire threat quantification from videos
Chakraborty, D. B., Detani, V., & Jigneshkumar, S. P. (2022) · Displays · doi:10.1016/j.displa.2021.102140 · Cited by 3
Asrul, A., Tamrin, F., & Arman, A. (2022) · Ceddi Journal of Information System and Technology (JST) · doi:10.56134/jst.v1i1.1 · Cited by 3
Predictive Modeling Classification of Post-Flood and Abrasion Effects With Deep Learning Approach
Marleny, F. D., & Mambang, M. (2022) · TIERS Information Technology Journal · doi:10.38043/tiers.v3i1.3604 · Cited by 3
“Disasters have types and characteristics[3], [4].”
Deep Neural Networks for Wild Fire Detection and Monitoring with UAV
Gewali, V., & Panday, S. P. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-25088-0_37 · Cited by 3
Deep Learning Model for Detection and Recognition of Fire based on Virtual Reality Video Images
Prasad, K. S. R., Rao, N. S., Babu, T. K., Pranav, A., Bobby, G., & Haribulla, S. (2023) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict57646.2023.10134475 · Cited by 3
“To address this issue, researchers have proposed various techniques, such as regularization, replay, and dynamic architecture modification, to mitigate the effects of catastrophic forgetting and enable continual learning [7].”
BackgroundPatronus: Fire Detection and Extinguisher System using Image Processing
Poornima, N., S, G. G., Bangar, A. S., Prasad, P. S. S., Pai, P., & Mehta, T. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142665 · Cited by 3
“Transfer learning technique is used to detect fire which significantly increases the accuracy based on deep CNN approach [10, 15, 20, 25].”
MethodUse of AI for Disaster Risk Reduction in Agriculture
Hammad, M., Shoaib, M., Salahudin, H., Baig, M. A. I., & Ali, M. U. (2023) · Disaster Resilience and Green Growth · doi:10.1007/978-981-99-1763-1_22 · Cited by 3
Improved Fire Safety in the Wildland-Urban Interface Through Smart Technologies
Cobian‐Iñiguez, J., Gollner, M. J., Saha, S., Avalos, J., & Ameri, E. (2024) · Digital innovations in architecture, engineering and construction · doi:10.1007/978-3-031-48161-1_8 · Cited by 3
Forest Fire Detection Using a Low Complex Convolutional Neural Network
Khirani, S., Souahlia, A., & Rabehi, A. (2023) · International Conference on Electronics, Energy and Measurement (IC2EM) · doi:10.1109/ic2em59347.2023.10453317 · Cited by 3
“Muhammad and colleagues [25] presented a fire detection framework that involved fine-tuning convolutional neural networks, whereas alternative approaches [26–29] employed neural networks to achieve early fire detection.”
Fireguard: Deep CNN Video Surveillance for Efficient Fire Detection
R, B. V., Shanthini, V. S., Sri Balaji, R., & Stinsha, S. L. (2024) · doi:10.1109/icstem61137.2024.10561206 · Cited by 3
WG-YOLOv5: improved YOLOv5 based on wavelet transform and GSConv for real-time wildfire detection
Lu, L., & Wang, H. (2024) · International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT) · doi:10.1117/12.3031068 · Cited by 3
“Muhammad et al. [5] used pretrained deep learning models, such as AlexNet and GoogleNet [6], to train and fine-tune on small fire datasets, achieving significant performance improvements.”
Enhanced Fire Detection Algorithm for Chemical Plants Using Modified YOLOv9 Architecture
Wang, Z. (2024) · International Conference on Electronic Technology and Information Science (ICETIS) · doi:10.1109/icetis61828.2024.10593793 · Cited by 3
“Muhammad et al. [3] undertook research aimed at refining CNN models to enhance early fire detection, integrating a prioritization system for each monitoring node.”
BackgroundSahebi, A., Sayyadi, H., Havasi, B., & Veisani, Y. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06703-0 · Cited by 3
“Many studies have employed data mining models to improve decision-making during emergency response to fires [16, 17], from forest fires to other emergency situations, in order to diminish human and environmental losses [18].”
Neural Networks Fire Detection in a Public Safety Fog System
Crețu, A., Radu, D., Nandor, K., Avram, C. C., Domuţă, C., & Aştilean, A. (2020) · IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR) · doi:10.1109/aqtr49680.2020.9129925 · Cited by 3
“While each technique has its pros and cons we could not help to notice that the neural network techniques (covered in [11], [12], [13], [14], [15], [16]) are considered to be suitable in terms of cost, mobility, information on fire behavior, small delay in fire detection notifications, and high fire…”
BackgroundMethodFlameNet: A Real-Time, Lightweight Fire & Smoke Detection Solution for Internet of Vehicles
Mondal, K. K., & Das, D. (2023) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc58020.2023.10182847 · Cited by 3
“AlexNet [12], SqueezeNet [16], GoogleNet [17], and MobileNetV2 [18] are only a few of the convolutional neural network (CNN) versions that Muhammad et al. Foggia’s dataset [19] was utilised extensively in the train datasets for [20], whereas [21]’s train dataset was a combination of [22] and [19].”
MethodStudy of Region Convolutional Neural Network Deep Learning for Fire Accident Detection
Damour, N. J., Chang, K., Li, P., Zhou, Y., Wang, H.-C., Lin, Y.-C., Chu, K.-C., & Hsu, T.-L. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-58669-0_13 · Cited by 3
Dark-Channel Mixed Attention Based Neural Networks for Smoke Detection in Fog Environment
Yang, L., Gong, X., Wu, Z., Han, Y., He, L., & Li, F. (2021) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3460418.3480403 · Cited by 3
“The deep learning based algorithms usually consider smoke detection task as image classification task [23, 25] or video classification task [17, 20].”
MethodAnchor-Free Smoke and Flame Recognition Algorithm with Multi-Loss
Li, G., Chen, P., Xu, C., Sun, C., & Ma, Y. (2023) · Fire · doi:10.3390/fire6060225 · Cited by 3
“Early fire recognition, warning, and autonomous response can effectively reduce fire damage [1].”
BackgroundRosyidiin, A. F., Puteri, A. H. A., Priyangga, D. Z., Ivander, D. D., & Liwaq, M. I. (2024) · Airlangga Journal of Innovation Management · doi:10.20473/ajim.v5i2.57551 · Cited by 2
IEFDNet: Detection of Fire Using Partial Convolution Inception
Chen, G.-S., Chandarasupsang, T., Luo, X., Tananchana, A., & Mu, L. (2024) · International Conference on Big Data Engineering and Education (BDEE) · doi:10.1109/bdee63226.2024.00013 · Cited by 2
Enhanced wildfire detection and semantic segmentation via fine-tuned deep learning models
Quang, N. H., Lee, H., Ahn, S., & Kim, G. (2025) · Earth Science Informatics · doi:10.1007/s12145-025-01863-4 · Cited by 2
“However, others recommended early WF detection by fine-tuned CNNs and closed-circuit television (CCTV) surveillance cameras would support an effective framework of effective fire disaster management (Muhammad et al. 2018).”
Deep-Learning-Driven Technique for Accurate Location of Fire Source in Aircraft Cargo Compartment
Zhu, Y., Li, C., Tang, S., Jia, X., Chen, X., Liu, Q., & Chow, W. K. (2025) · Fire · doi:10.3390/fire8080287 · Cited by 2
ConFirePatch: Context-based fire detection and segmentation via extracting key patches
Li, R., Wang, Y., & Tu, X. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.07.044 · Cited by 2
A Survey on Emergency Alert System for Deaf People using AI
Indu, B., Deekshitha, G., Varshita, V. A., Austin Masila, A., & Mansoor, S. (2025) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds65426.2025.11167516 · Cited by 2
Prediction of Tsunami Event at Early Stage Using Ensemble Bagging Random Forest Classifier
Sarika, A. S., & Milton, A. (2025) · Journal of Earthquake and Tsunami · doi:10.1142/s1793431125500265 · Cited by 2
A Lightweight Fire Detection Framework for Edge Visual Sensors Using Small-Sample Domain Adaptation
Hu, J., Yao, R., Yang, Q., Ding, Y., Zhang, L., & Liu, J. (2026) · Sensors · doi:10.3390/s26041121 · Cited by 2
“…systems (CCTV) [4], computer vision-based flame detection technology has emerged as a research hotspot and a frontier direction in the current fire warning field, owing to its inherent advantages of non-contact detection, rapid response, wide coverage, and visual verification capabilities [5–7].”
Ali, M. M. E. H., Tahtali, M., & Ghodrat, M. (2026) · Fire Safety Journal · doi:10.1016/j.firesaf.2026.104789 · Cited by 2
An Effective Approach for Object Detection Using Deep Convolutional Networks
Kumar, C. K., Sethi, G., & Rawal, K. (2022) · International Conference on Breakthrough in Heuristics and Reciprocation of Advanced Technologies (BHARAT) · doi:10.1109/bharat53139.2022.00019 · Cited by 2
Forest Fire Detection using Convolutional Neural Network Model
Sah, S., Prakash, S., & Meena, S. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126370 · Cited by 2
Essien, J. (2023) · Journal of Computer and Communications · doi:10.4236/jcc.2023.117009 · Cited by 2
Fire Detection Warning System in House Fire Accident Prevention
Prakoso, B. A., Pahlevi, R. R., Sthevanie, F., & Utomo, R. G. (2023) · doi:10.1109/icoict58202.2023.10262552 · Cited by 2
Kesiapsiagaan Masyarakat dalam Menghadapi Ancaman Kebakaran
Hasna, A. M., Dahlia, S., Harsono, R. T. N., & Adiputra, A. (2023) · JAMBURA GEO EDUCATION JOURNAL · doi:10.34312/jgej.v4i2.20933 · Cited by 2
Raw Spectral Filter Array Imaging for Scene Recognition
Askary, H., Hardeberg, J. Y., & Thomas, J. (2024) · Sensors · doi:10.3390/s24061961 · Cited by 2
Research on the influence of emergency rescue information importance on emergency decisions
Jiang, W., Zhou, J., Ma, S., Wu, Z., Cui, X., & Yang, C. (2024) · Journal of Contingencies and Crisis Management · doi:10.1111/1468-5973.12582 · Cited by 2
A new strategy for fire detection based on analyzing the irregularity pattern of image contour
Oghabi, E., Moghaddam, R. K., & Kobravi, H.-R. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03303-0 · Cited by 3
INTELLIGENT SURVEILLANCE SYSTEM FOR FIRE DETECTION USING YOLOV8
Mohammed, M. S., Abbas, A. H., & Abdullah, N. A. Z. (2024) · Iraqi Journal for Computers and Informatics · doi:10.25195/ijci.v50i1.476 · Cited by 2
A Context-Oriented Multi-Scale Neural Network for Fire Segmentation
Zhang, T., & Dick, R. P. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647443 · Cited by 2
A Survey of Vision-based Fire Detection using Convolutional Neural Networks
Chen, G.-S., Chandarasupsang, T., Luo, X., Tananchana, A., & Mu, L. (2024) · International Conference on Big Data Engineering and Education (BDEE) · doi:10.1109/bdee63226.2024.00015 · Cited by 3
“The BoWFire dataset is one of the most commonly used datasets to validate the effectiveness and generalization of fire detection models, as shown in studies such as [10], [11], [12], and [13].”
MethodInnovative System Using Artificial Intelligence and Robotics for Fire Disaster Management
Komala, C. R., Prakash, S., Yogesh, G. S., Thivakaran, T. K., & Ashwin, N. (2024) · Advances in social networking and online communities book series · doi:10.4018/979-8-3693-2337-3.ch010 · Cited by 2
Fire Detection Based on Flame Enhancement for Weak Fires
Chen, K., Wen, W., Feng, F., Xu, X. M., & Liang, Y. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-9919-0_9 · Cited by 2
Current developments and trends in video surveillance
Isa, A. M., & Al‐Turjman, F. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26482-5.00002-x · Cited by 2
Bhowmick, S. K., Barman, A., & Roy, S. K. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105068 · Cited by 2
Virtual Disaster Management Systems for Real-Time Response and Aid
RAVISANKAR, S., Ramachandran, V., & R, S. S. (2025) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai65575.2025.11011803 · Cited by 2
“Improved adaptive algorithms would first apply to flood, earthquake and fire disaster response[26].”
Analysis of Deep Learning Techniques Used for Indoor Flame Detection
Kundu, T., & Senapati, K. K. (2022) · International Conference on IoT and Blockchain Technology (ICIBT) · doi:10.1109/icibt52874.2022.9807802 · Cited by 2
An Analysis of Parameters of Convolutional Neural Network for Fire Detection
Son, G.-Y., Маршалл, Park, J., & Lee, D. (2019) · International Conference on Software Engineering and Information Management (ICSIM) · doi:10.1145/3305160.3305197 · Cited by 2
Surveillance, Fire Detection and Protection System for Substations
Satheesh, A. (2020) · International Journal of Engineering Research and · doi:10.17577/ijertv9is070247 · Cited by 2
“[1][2] Python is dynamically typed and garbage-collected.”
BackgroundMethodInfluentialAn Integrated Smoke Detection Method based on Convolutional Neural Network and Image Processing
Ma, P., Yu, F. Q., Zhou, C., & Jiang, M. (2020) · International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt50940.2020.9304985 · Cited by 2
GÜLLÜCE, Y. (2021) · Natural Hazards · doi:10.1007/s11069-021-04901-8 · Cited by 2
Analysis Model of Human Resource Cross-Media Fusion Based on Deep Neural Network
Ma, S., Xuan, S., & Liang, Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/6069589 · Cited by 2
Microscopic identification of brazilian commercial wood species via machine-learning
Moulin, J. C., Lopes, D. J. V., Mulin, L. B., Bobadilha, G. D. S., & Oliveira, R. F. (2022) · CERNE · doi:10.1590/01047760202228012978 · Cited by 2
YOLOv6 for Fire Images detection
Jabnouni, H., Arfaoui, I., Cherni, M. A., Bouchouicha, M., & Sayadi, M. (2023) · International Conference on Cyberworlds (CW) · doi:10.1109/cw58918.2023.00086 · Cited by 2
“Muham-mad et al [1] have introduced an affordable fire detection architecture based on convolutional neural networks (CNN) for surveillance videos.”
The monitoring of burning buildings with convolutional neural network
Batog, F., & Moldovanu, S. (2023) · SYSTEM THEORY CONTROL AND COMPUTING JOURNAL · doi:10.52846/stccj.2023.3.2.50 · Cited by 2
“Among such abnormal events, fire is one of the commonly happening events, whose detection at early stages during surveillance can avoid home fires or fire disasters [1].”
BackgroundFireNet-Tiny: Very-Low Parameter Count High Performance Fire Detection Model
Oyebanji, O. J., Oliver, S., Ogonna, C. E., Aslam, A., & Ansari, M. S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-7814-4_40 · Cited by 2
Fire Threat Detection From Videos with Q-Rough SetsPreprint
Chakraborty, D. B., Detani, V., & Jigneshkumar, S. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.08459 · Cited by 2
“[19] came up with a solution by incorporationg deep features of CNNs in fire-detection and high-priority cameras based on cognitive radio networks were developed.”
MethodSridhar, P., Thangavel, S. K., Parameswaran, L., & Oruganti, V. R. M. (2022) · Research Square · doi:10.21203/rs.3.rs-1524107/v1 · Cited by 2
Early Fire Detection using LSTM based Instance Segmentation and IoTs for Disaster ManagementPreprint
Malebary, S. J. (2023) · Preprints.org · doi:10.20944/preprints202310.0065.v1 · Cited by 2
Verma, P., & Bakthula, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17722-7 · Cited by 1
Arunkumar, R., & Dubey, A. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00260 · Cited by 1
“A study by Muhammad et al. adjusted CNN architectures trained on general images to detect fires from CCTV footage, showcasing the adaptability of deep learning models through transfer learning [20].”
Method“Deep Learning-Driven Fire Detection Through Advanced Image Processing Techniques”
Shahila, D. F. D., Stephen, V., Rama, S., Kumar, R. J. R., Ashwini, A., & Hanumanthu, Y. (2025) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct65132.2025.11176529 · Cited by 1
“It delineates the fire as well [5].”
Khan, R. A., Bajwa, U. I., Raza, R. H., & Farooq, M. U. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.113996 · Cited by 1
Metadata
- Authors
- Muhammad, K., Ahmad, J., & Baik, S. W.
- Affiliations
- Sejong University (KR)
- Corresponding author
- Baik, Sung Wook
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 288
- Pages
- 30-42
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2780222614
- Cited by
- 558
- References
- 68
- Citations by year
- 2026: 40 · 2025: 62 · 2024: 78 · 2023: 120 · 2022: 87 · 2021: 60 · 2020: 51 · 2019: 43 · 2018: 17
- FWCI
- 44.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- National Research Foundation of Korea (NRF) (2016R1A2B4011712)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Fire Detection and Safety Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Fire dynamics and safety research
- Keywords
- early fire detection, CCTV surveillance, convolutional neural networks, fine-tuning, disaster management, dynamic channel selection, cognitive radio networks, autonomous response
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